Graphical mapping of pipe node location selection

ABSTRACT

Examples for creating a graphical mapping of pipe node location selection for a fluid distribution system are disclosed. In one example implementation according to aspects of the present disclosure, a method for creating a graphical mapping of pipe node location selection for a fluid distribution system includes: receiving predetermined criteria for each pipe segment of a plurality of pipe segments in the fluid distribution system; determining an equivalent length for each pipe segment based at least on the predetermined criteria; grouping each pipe segment into a specific propagation category of a plurality of propagation categories based on the equivalent lengths; and creating a graphical map of the plurality of pipe segments and utility components utilizing a plurality of links.

BACKGROUND

A utility provider may install and maintain infrastructure to provideutility services to its customers. For example, a water utility providermay implement a fluid distribution system to distribute water to itscustomers. Metering devices may be utilized by the utility provider todetermine consumption of the provided utility (e.g., water, electricity,gas, etc.). The utility provider may implement various devices orcomputing nodes throughout the fluid distribution system to monitor thestatus of the fluid distribution system, including condition assessmentfor the pipes used therein, predicting attenuation based on type of pipeand type of surrounding soil, and graphically mapping efficient layoutsof the computing node locations based on propagating distances.

Due to the rapidly escalating costs of potable water, the scarcity offresh water supplies, the increasing costs for water treatment anddistribution, and the potential for costly damage to subsurfaceinfrastructure, accurate condition assessment and minimizing leaks inwater distribution systems is a goal of both public and private waterdistribution utilities. If a leak is not particularly conspicuous, itmay go undetected for months at a time without repair. It is thereforeimportant to be able to assess pipe degradation early before leaks.

Several techniques for condition assessment currently exist for directcondition assessment, including visual inspection, leak detectionsystems, wall thickness measurements, soil testing, corrosionmonitoring, and analyzing break history in similar pipes in the networkof water pipes. Leak detection systems utilizing acoustic monitoring canalso be used to perform condition assessment by providing an indicationof average wall thickness between two measuring points. These acousticmonitoring systems are good screening tools for detecting widespreadcorrosion and wall loss, they are non-intrusive, and generally are lowcost. However, current techniques utilizing acoustic monitoring are notreliable and may still require unnecessary and costly visual inspection.There is therefore a need for a condition assessment system thataccurately determines condition assessment in a network of water pipeswithout having to rely on visual inspection. Furthermore, there is alsoa need that enables reliable placement for computing nodes for a fluiddistribution system by utilizing graphical mapping and acousticalunderstanding of sound propagation in the pipe network.

SUMMARY

It is to be understood that this summary is not an extensive overview ofthe disclosure. This summary is exemplary and not restrictive, and it isintended to neither identify key or critical elements of the disclosurenor delineate the scope thereof. The sole purpose of this summary is toexplain and exemplify certain concepts of the disclosure as anintroduction to the following complete and extensive detaileddescription.

The present disclosure relates to collecting and analyzing data in afluid distribution system to determine efficient and reliable locationof computing nodes within a fluid distribution system utilizinggraphical mapping. According to some aspects, a method for creating agraphical mapping of pipe node location selection for a fluiddistribution system comprises receiving predetermined criteria for eachpipe segment of a plurality of pipe segments in the fluid distributionsystem. Each pipe segment may comprise a section of pipe between twoutility components of the fluid distribution system. Each utilitycomponent may be configured to engage with a computing node configuredfor leak detection via acoustical propagation. An equivalent length foreach pipe segment based at least on the predetermined criteria is thendetermined. The method further comprises grouping each pipe segment intoa specific propagation category of a plurality of propagation categoriesbased on the equivalent lengths. Finally, the method comprises creatinga graphical map of the plurality of pipe segments and utility componentsutilizing a plurality of links. The plurality of links may comprisedistinct visual indications based on the propagation category for eachpipe segment, where each utility component may comprise a correspondingcomputing node.

According to further aspects, a system for creating a graphical mappingof pipe node location selection for a fluid distribution systemcomprises a plurality of computing nodes and a computing host incommunication with the plurality of computing nodes. The plurality ofcomputing nodes are in fluid communication with the fluid distributionsystem and configured to acquire acoustic data in the fluid distributionsystem. The computing host is programmed to perform steps. The firststep comprises receiving predetermined criteria for each pipe segment ofa plurality of pipe segments in the fluid distribution system. Each pipesegment may comprise a section of pipe between two utility components ofthe fluid distribution system. Each utility component may be configuredto engage with a computing node configured for leak detection viaacoustical propagation. An equivalent length for each pipe segment basedat least on the predetermined criteria is then determined in the nextstep. The next step comprises grouping each pipe segment into a specificpropagation category of a plurality of propagation categories based onthe equivalent lengths. Finally, the last step comprises creating agraphical map of the plurality of pipe segments and utility componentsutilizing a plurality of links. The plurality of links may comprisedistinct visual indications based on the propagation category for eachpipe segment, where each utility component may comprise a correspondingcomputing node.

According to further aspects, a non-transitory computer-readable storagemedium storing instructions that, when executed by a processingresource, cause the processing resource to perform steps. The first stepcomprises receiving pipe segment criteria for a pipe segment. The pipesegment comprises receiving predetermined criteria for each pipe segmentof a plurality of pipe segments in the fluid distribution system. Eachpipe segment may comprise a section of pipe between two utilitycomponents of the fluid distribution system. Each utility component maybe configured to engage with a computing node configured for leakdetection via acoustical propagation. An equivalent length for each pipesegment based at least on the predetermined criteria is then determinedin the next step. The next step comprises grouping each pipe segmentinto a specific propagation category of a plurality of propagationcategories based on the equivalent lengths. Finally, the last stepcomprises creating a graphical map of the plurality of pipe segments andutility components utilizing a plurality of links. The plurality oflinks may comprise distinct visual indications based on the propagationcategory for each pipe segment, where each utility component maycomprise a corresponding computing node.

These and other features and aspects of the various aspects will becomeapparent upon reading the following Detailed Description and reviewingthe accompanying drawings. Furthermore, other examples are described inthe present disclosure. It should be understood that the features of thedisclosed examples can be combined in various combinations. It shouldalso be understood that certain features can be omitted while otherfeatures can be added.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following Detailed Description, references are made to theaccompanying drawings that form a part hereof, and that show, by way ofillustration, specific aspects or examples. Any illustrated connectionpathways in block and/or circuit diagrams are provided for purposes ofillustration and not of limitation, and some components and/orinterconnections may be omitted for purposes of clarity. The drawingsherein are not drawn to scale. Like numerals represent like elementsthroughout the several figures.

FIG. 1 illustrates a diagram of an environment to collect and analyzeacoustic data for condition assessment and leak detection within a fluiddistribution system according to examples of the present disclosure.

FIG. 2 illustrates a block diagram of a computing node to collect andanalyze acoustic data for condition assessment and leak detection withina fluid distribution system according to examples of the presentdisclosure.

FIG. 3 illustrates a computing system including a computer-readablestorage medium storing instructions to analyze the data collected withina fluid distribution system according to examples of the presentdisclosure.

FIG. 4 illustrates a diagram of a fluid distribution system with a noisesource and computing nodes attached to components of the fluiddistribution system for collecting and analyzing acoustic data forcondition assessment and leak detection according to examples of thepresent disclosure.

FIG. 5 illustrates a flow diagram of a method to analyze data collectedwithin a fluid distribution system and determine pipe degradationutilizing predicted frequency content according to examples of thepresent disclosure.

FIGS. 6A-6B illustrate graphs of data of measured frequency content datarelating to the techniques for collecting and analyzing data andcreating prediction models within a fluid distribution system accordingto examples of the present disclosure.

FIGS. 6C-6D illustrate graphs of data of predicted frequency contentdata relating to the techniques for collecting and analyzing data andcreating prediction models within a fluid distribution system accordingto examples of the present disclosure.

FIG. 7 illustrates a graph of data relating to the techniques forcollecting and analyzing data and creating prediction models within afluid distribution system according to examples of the presentdisclosure.

FIG. 8 illustrates a screen diagram of a user interface to analyze datacollected within a fluid distribution system and determine pipedegradation utilizing predicted frequency content according to examplesof the present disclosure.

FIG. 9 illustrates a flow diagram of a method to collect and analyzedata to generate and utilize pipe-specific sound attenuation rangeswithin a fluid distribution system according to examples of the presentdisclosure.

FIG. 10 illustrates an example colormap relating to the techniques forcollecting and analyzing data to generate and utilize pipe-specificsound attenuation ranges within a fluid distribution system according toexamples of the present disclosure.

FIGS. 11A-11B illustrate example colormaps relating to the techniquesfor collecting and analyzing data to generate and utilize pipe-specificsound attenuation ranges within a fluid distribution system according toexamples of the present disclosure.

FIGS. 12A-12B illustrate example colormaps relating to the techniquesfor collecting and analyzing data to generate and utilize pipe-specificsound attenuation ranges within a fluid distribution system according toexamples of the present disclosure.

FIG. 13A-13B illustrate example colormaps relating to the techniques forcollecting and analyzing data to generate and utilize pipe-specificsound attenuation ranges within a fluid distribution system according toexamples of the present disclosure.

FIG. 14 illustrates a graph of example propagating distances fordifferent types of pipes relating to the techniques for collecting andanalyzing data to generate and utilize pipe-specific sound attenuationranges within a fluid distribution system according to examples of thepresent disclosure.

FIG. 15 illustrates a flow diagram of a method to analyze data collectedwithin a fluid distribution system and determine computing node locationselection utilizing graphical mapping according to examples of thepresent disclosure.

FIGS. 16A-16C illustrate a diagram of a fluid distribution system withcomputing nodes for collecting and analyzing acoustic data for graphicalmapping of computing node location selection within the fluiddistribution system according to examples of the present disclosure.

DETAILED DESCRIPTION

Various implementations are described below by referring to severalexamples of collecting and analyzing acoustic data in a fluiddistribution system. In examples, the water utility provider may deploydevices (nodes) across the fluid distribution system to collect datarelating to the network of pipes. The data may then be analyzed todetermine pipe degradation based on loss from the pipe wall thickness,predict pipe attenuation based on the type of pipe and soil type, ordetermine efficient node location utilizing graphical mapping. Otherexample implementations and variations are disclosed herein.

The present disclosure enables reliable condition assessment for a fluiddistribution system by utilizing sound propagation comparison withautomated frequency selection. In acoustics, from a theoreticalperspective, the frequency content of cylindrical waveguides isrelatively known and is dependent on several parameters. Water pipes maybe considered as cylindrical waveguides; therefore, their fundamentalfrequency content for acoustic propagation may be predicted usingparameters such as the diameter, the thickness of the wall, the distanceof propagation, the attenuation of the pipe material, and so forth. Inreal water networks, pipes depict a vibro-acoustical behavior which isnot just that of cylindrical waveguides. Certain conditions tend tomodify the vibro-acoustic behavior of pipes (pipe supports, localstiffeners, pipe junctions, etc.). These changes, induced in thevibro-acoustic behavior of the pipes, make the analysis of sound filesmore complicated. When analyzing the behavior of pipes using theirfrequency content, errors can be present in the results due to themodifications in behavior explained before. Thus, selecting the wrongfrequency content will lead to incorrect results and wrong predictions.To avoid these mistakes in interpreting the frequency content ofcorrupted sound files, the present disclosure considers the pipe as aperfect cylindrical waveguide and uses the equivalent theoreticalfrequency content for analysis.

According to aspects described herein, an acoustical model utilizingspecific parameters such as the pipe diameter, the wall thickness, thematerial and its mechanical characteristics, the distance betweensensors (nodes), and the attenuation to predict the frequency content ofmeasurement between acoustical sensors may be used. According to furtheraspects described herein, the acoustical model may be simplified as amathematical formula which can predict a range of frequency.

The present disclosure further enables reliable detection for predictinga more precise distance a leak noise will propagate within a pipenetwork. Once a leak forms in a water network, a noise can be detectedin the proximity of the leak. How far from the leak location this noisecan be detected is valuable information for water distributionutilities. It is known that the noise generated by a leak is dependenton multiple parameters such as the leak size, the pressure, the pipespecific dimensions and material, the attenuation from the soil, and thelike. However, no model allows to predict the sound level generated by aspecific leak. Therefore, it is difficult to predict the exact distancea leak noise will propagate within a pipe network. The presentdisclosure describes a more reliable method to evaluate and predict amore precise distance a noise will propagate in a specific pipe.

According to aspects described herein, the distance the noise generatedby a leak may propagate in a water network may be dependent on multipleparameters including elements specific to the pipe networks, andstatistical parameters obtained with measurement on various sites.According to aspects described herein, utilizing a combination ofparameters specific to the pipe, parameters from literature review, e.g.research articles, to take into account the soil attenuation, andparameters from measurement on various sites, a statistically possibleleak noise measured by a sound level may be determined. According toaspects described herein, a mathematical formula may be used thatintegrates several parameters, either entered by a user or predefined inthe system, into a computing host to predict the distance a leak noisemay propagate for a specific type of pipe. According to aspectsdescribed herein, a range of distance for possible propagation alongwith the frequency dependent acoustical attenuation may be calculatedand displayed as a colormap. According to aspects described herein, acolormap may allow more precise prediction of the frequency ofpropagation and the distance the noise should propagate for differenttype of pipes and soil configurations.

The present disclosure further enables reliable placement for computingnodes for a fluid distribution system by utilizing graphical mapping andacoustical understanding of sound propagation in the pipe network. Theinstallation of an acoustic propagation detection system, such as theECHOLOGICS® ECHOSHORE®-DX leak detection system, necessitates theinstallation of computing nodes on hydrants, or other components of thepipe network, to create a network of acoustical sensors to detect leaks.Determining which hydrant should receive a computing node requiresseveral hours of manual work, and the operator would have to look at amap to select the hydrants where computing nodes should be installed.This selection process of hydrants typically take days for a large site.An automatic selection process is needed to simplify the selection andinstallation processes, especially for large scale deployment of anacoustic propagation detection system. This automatic process requiresan acoustical understanding of how far a leak noise can propagate to beable to place the computing node adequately. The present disclosureallows to save time, reduce the manual effort, and removes thesubjective decision making of an operator in selecting a location foreach computing node.

According to further aspects described herein, an automated selectionprocess may be used that requires an acoustical understanding of how fara water leak noise can propagate to be able to place a computing nodeadequately combined with a geographic information system (GIS) in orderto identify the possible locations for installation of computing nodesin a pipe network. According to further aspects described herein, theselection process may be reduced from days to minutes. According tofurther aspects described herein, for a given geographical area, thesystem may automatically identify if the distance between two waterhydrants allows adequate acoustical propagation for leak detection. Forexample, if the distance will be acoustically covered, the system maydisplay the corresponding pipe segment in Green, or depict the pipesegment as a solid line. If the distance may be covered, dependent onattenuation and pipe condition, the system may display the correspondingpipe segment in Yellow, or depict the pipe segment as a dashed line. Ifthe distance is too long to ensure propagation for the given type ofpipe, the segment may be displayed in Red, or depict the pipe segment asa dotted line. It will be appreciated by one skilled in the art thatanother visual indication to decipher the three different labeled lineson a map for the propagation distances may be used.

FIGS. 1-3 illustrate particular components, modules, instructions,engines, etc. according to various examples as described herein. Indifferent implementations, more, fewer, and/or other components,modules, instructions, engines, arrangements ofcomponents/modules/instructions/engines, etc. may be used according tothe teachings described herein. In addition, various components,modules, engines, etc. described herein may be implemented asinstructions stored on a computer-readable storage medium, as hardwaremodules, as special-purpose hardware (e.g., application specifichardware, application specific integrated circuits (ASICs), as embeddedcontrollers, hardwired circuitry, etc.), or as some combination orcombinations of these.

Generally, FIGS. 1-3 relate to components and modules of a computingsystem, such as computing host 120 of FIG. 1 and computing host 320 ofFIG. 3 as well as components and modules of a computing node, such ascomputing nodes 150A-150N (also referred to herein generally ascomputing nodes 150) of FIG. 1 and computing node 250 of FIG. 2. Itshould be understood that the computing hosts and/or computing nodes maycomprise any appropriate type of computing system and/or computingdevice, including for example smartphones, tablets, desktops, laptops,workstations, servers, smart monitors, smart televisions, digitalsignage, scientific instruments, retail point of sale devices, videowalls, imaging devices, peripherals, networking equipment, wearablecomputing devices, metering devices, data collection devices, leakdetecting devices, or the like.

FIG. 1 illustrates a diagram of an environment 100 to collect andanalyze acoustic data within a fluid distribution system 110, accordingto examples of the present disclosure. As will be further describedherein, computing nodes 150 of FIG. 1 collect and analyze acoustic datarelating to the fluid distribution system 110. The acoustic datacollected by the computing nodes 150 is transmitted to computing host120 which performs analysis on the received data to determine conditionassessment such as pipe degradation based on loss from the pipe wallthickness, predict pipe attenuation based on the type of pipe and soiltype, and/or determine efficient and reliable location utilizinggraphical mapping.

As illustrated, the environment 100 comprises the fluid distributionsystem 110, which may further comprise various components such as pipes,hydrants, valve, couplers, corporation stops, metering devices, etc.Although illustrated as a pipe, it should be understood that the fluiddistribution system 110 may be a plurality of pipes and other fluiddistribution system components connected together to form the fluiddistribution system 110, of which the pipe is a portion.

Generally, the fluid distribution system 110 may be used to distributefluids such as water to customers of a utility provider, for example.The fluid distribution system 110 may comprise various and numerouscomponents, such as pipes, hydrants, valves, couplers, corporationstops, metering devices, and the like, as well as suitable combinationsthereof. In examples, the fluid distribution system 110 may be partiallyor wholly subterraneous, or portions of the fluid distribution system110 may be subterraneous, while other portions of the fluid distributionsystem 110 may be non-subterraneous (i.e., above ground). For example, acomponent of the fluid distribution system 110 may be partially orwholly subterraneous while another component (e.g., a hydrant, a valve,a testing device, etc.) connected to the first component and may bepartially or wholly non-subterraneous. In other examples, the componentmay be partially subterraneous in that the component has portionsexposed, such as to connect certain devices (e.g., computing nodes 150,a hydrant, a valve, a testing device, etc.) to the fluid distributionsystem 110.

The computing nodes 150 monitor certain aspects of the fluiddistribution system 110 and/or aspects of a fluid flowing through thefluid distribution system 110, illustrated as fluid path 112 within thefluid distribution system 110. In examples, the computing nodes 150 arein fluid communication with fluid path 112 within the fluid distributionsystem 110. In other examples, the computing nodes 150 are connected toa component of the fluid distribution system 110 and are not in fluidcommunication with the fluid path 112. As illustrated in FIG. 1, thecomputing nodes 150 are connected to a pipe of the fluid distributionsystem 110. In examples, the connection may be direct and/or indirect.More particularly, the computing nodes 150 may be connected directly toa pipe of the fluid distribution system 110, such as through a holedrilled into the wall of the pipe or via a coupling member (not shown)of the pipe, thereby causing the computing nodes 150 (or a sensor of thecomputing nodes 150) to be in fluid communication with the fluid path112. In another example, computing nodes 150 may be connected indirectlyto the pipe, such as via another component in the fluid distributionsystem 110 (e.g., a hydrant, a valve, a coupler, a corporation stop,metering device, etc.). Although four computing nodes 150A-N areillustrated, it should be understood that any suitable number ofcomputing nodes are possible in various examples. In examples, thecomputing nodes 150 are placed in or connected to existing components ofthe fluid distribution system 110, such as a fire hydrant. A computingnode 150 may be connected to each fire hydrant within a fluiddistribution system, for example, or may be placed within a certaindistance of another node (e.g., within 500 feet, within 1500 feet, etc.)

The computing nodes 150 collect and analyze acoustic data concerning thefluid distribution system 110. For example, the computing nodes 150 maycollect a first acoustic data set synchronized with a known timereference for the purpose of detecting and locating a leak throughcorrelation. The first acoustic data set may be compressed beforetransmission. The computing nodes 150 may then collect a second acousticdata set, which may include multiple acoustic data recordings todiscriminate between persistent and transient processes. The secondacoustic data set may then be analyzed by computing host 120 todetermine if a leak is present, determine condition assessment such aspipe degradation percentage based on loss from the pipe wall thickness,predict pipe attenuation based on the type of pipe and soil type, and/ordetermine efficient and reliable location by implementing a graphicalmap.

As described below regarding FIG. 3, the computing nodes 150 maycomprise various components, modules, engines, etc., such as an acousticdata collection module, an acoustic data analysis module, an acousticdata compression module, an acoustic data transmission module, a powersupply, a data transmitter, a data receiver, an antenna, etc. The datatransmitter, data receiver, and/or antenna may be used to wirelesslytransmit and/or receive signals, commands, and/or data to and from otherdevices, including the computing host 120 such as via the network 140and a communications hub 142 across communication links 144-148.

The dotted lines of FIG. 1 illustrate communicative links between andamong the computing nodes 150, the communication hub 142, and thecomputing host 120, including a communication link 144 (between thecommunication hub 142 and the computing host 120), a communication link146 (between the communication hub 142 and a network 140), andcommunication links 148 (between the network 140 and the computing nodes150). These links generally represent a network or networks that maycomprise hardware components and computers interconnected bycommunications channels that enable sharing of resources andinformation. The network 140 may comprise one or more of a cable,wireless, fiber optic, or remote connection via a telecommunicationlink, an infrared link, a radio frequency link, a cellular link, aBluetooth® link, or any other suitable connectors or systems thatprovide electronic communication. The network 140 may comprise, at leastin part, an intranet, the internet, or a combination of both. Thenetwork 140 may also comprise intermediate proxies, routers, switches,load balancers, and the like. The paths followed by the network betweenthe devices as depicted in FIG. 1 represent the logical communicationlinks between the computing nodes 150, the communication hub 142, thenetwork 140, and the computing host 120, not necessarily the physicalpaths or links between and among the devices.

The communication hub 142 may include a precise time reference such asglobal positioning system (“GPS”) coordinates, and distributes the timeinformation throughout the network. In other aspects, each computingnode 150 may include a time reference such as GPS coordinates. Thecomputing nodes 150 collect and analyze acoustic data, as describedherein. Each day, at specified times and periods, the computing nodes150 may collect acoustic data and send information regarding thecollected and analyzed data to the computing host 120.

The computing host 120 may comprise a processing resource 122 thatrepresents generally any suitable type or form of processing unit orunits capable of processing data or interpreting and executinginstructions. The processing resource 122 may be one or more centralprocessing units (CPUs), microprocessors, and/or other hardware devicessuitable for retrieval and execution of instructions. The instructionsmay be stored, for example, on a memory resource (not shown), such as acomputer-readable storage medium 330 of FIG. 3, which may comprise anyelectronic, magnetic, optical, or other physical storage device thatstore executable instructions. Thus, the memory resource may be, forexample, random access memory (RAM), electrically-erasable programmableread-only memory (EPPROM), a storage drive, an optical disk, and anyother suitable type of volatile or non-volatile memory that storesinstructions to cause a programmable processor (e.g., the processingresource 122) to perform the techniques described herein. In examples,the memory resource comprises a main memory, such as a RAM in which theinstructions may be stored during runtime, and a secondary memory, suchas a nonvolatile memory in which a copy of the instructions is stored.

Additionally, the computing host 120 may comprise an analysis engine 124which is configured to analyze acoustic data received from the computingnodes 150. In examples, the engine(s) described herein may be acombination of hardware and programming. The programming may beprocessor executable instructions stored on a tangible memory, and thehardware may comprise processing resource 122 for executing thoseinstructions. Thus a memory resource (not shown) can be said to storeprogram instructions that when executed by the processing resource 122implement the engines described herein. Other engines may also beutilized to include other features and functionality described in otherexamples herein.

Alternatively or additionally, the computing host 120 may comprisededicated hardware, such as one or more integrated circuits, ApplicationSpecific Integrated Circuits (ASICs), Application Specific SpecialProcessors (ASSPs), Field Programmable Gate Arrays (FPGAs), or anycombination of the foregoing examples of dedicated hardware, forperforming the techniques described herein. In some implementations,multiple processing resources (or processing resources utilizingmultiple processing cores) may be used, as appropriate, along withmultiple memory resources and/or types of memory resources.

The analysis engine 124 is configured to perform various analyses of thedata received from the computing nodes 150. For example, each day, whenthe computing nodes 150 send information regarding the collected andanalyzed data to the computing host 120, the computing host 120 analyzesthe received data. Objectives of the analysis are to determine pipedegradation based on loss from the pipe wall thickness, predict pipeattenuation based on the type of pipe and soil type, and/or determineefficient and reliable location utilizing graphical mapping. Theanalysis engine 124 may determine adjacencies among the computing nodes150 and perform correlation of the acoustic data for adjacent nodes(e.g., nodes within adjacencies). The correlation may include analyzingacoustic data received from adjacent nodes. According to some aspects,the correlation analysis may use any known method in the art. Thecomputing host 120 may comprise additional engines, such as a datareceiving engine to receive data from the computing nodes 150. The datamay comprise raw acoustic data, and compressed acoustic data.

Although not shown in FIG. 1, it should be appreciated that thecomputing host 120 may comprise additional components. For example, thecomputing host 120 may comprise a display. The display may comprise amonitor, a touchscreen, a projection device, and/or a touch/sensorydisplay device. The display may display data in the form of text,images, and other appropriate graphical content. The computing host 120may further comprise a network interface to enable the computing host120 to communicate via the communication link 148 with the computingnodes 150, with additional computing nodes, with other computingsystems, and/or with other suitable devices. The computing host 120 mayfurther implement a web server and a corresponding web application thatallows multiple users to visualize the aforementioned data and configurethe system remotely, over the network. The computing host 120 alsoimplements a notification system that may notify users of a relevantevent. The computing host 120 may also comprise any suitable inputand/or output device, such as a mouse, keyboard, printer, external diskdrive, touchscreen, microphone, or the like. The computing host 120 mayalso comprise an antenna (not shown) to wirelessly transmit and/orreceive signals, commands, and/or data to and from other devices,including the computing nodes 150 such as via the communication hub 142and the network 140 across the communication links 144-148.

FIG. 2 illustrates a block diagram of a computing node 250 to collectand analyze acoustic data within a fluid distribution system, such asfluid distribution system 110, according to examples of the presentdisclosure. The computing node 250 may represent any of computing nodes150 of FIG. 1 and/or the computing nodes illustrated in FIGS. 4 and16A-C. The computing node 250 monitors certain aspects of the fluiddistribution system and/or aspects of a fluid flowing through the fluiddistribution system. In examples, the computing node 250 is in fluidcommunication with the fluid path 112 within the fluid distributionsystem. In other examples, the computing node 250 is connected to acomponent of the fluid distribution system that is not in fluidcommunication with the fluid path 112.

In examples, the computing node 250 may comprise various components,modules, engines, etc., such as a processor 210, an acoustic datacollection module 262, an acoustic data analysis module 264, a storagemodule 266, and a communications module 268. The processor 210 maycomprise one or more of a microcontroller unit (MCU), a digital signalprocessor (DSP), and other processing elements.

The acoustic data collection module 262 may collect a first acousticdata at the computing node 250. The acoustic data collection module 262also collects second acoustic data at the computing node 250. Theacoustic data may be collected using a sensor or sensors of thecomputing node 250. Although not illustrated, the computing node 250 maycomprise a piezoelectric sensor, hydrophone, or other similar sensor todetect an acoustic signal. The acoustic signal is then collected by theacoustic data collection module 262 as acoustic data (e.g., firstacoustic data, second acoustic data, etc.). According to furtheraspects, the acoustic data analysis module 264 may analyze the acousticdata by comparing the collected second acoustic data to referenceacoustic data, as well as perform other data analysis on the acousticdata as described herein.

The storage module 266 may include flash memory, read-only memory (ROM),random access memory (RAM), or other types of memory. The storage module266 may comprise a database for storing acoustic data collected by theacoustic data collection module 262. The database may include frequencybins for storing current acoustic data as well as historic datacollected over several days. According to some aspects, the processor210 may be configured to utilize the stored acoustic data to detect thepresence or probability of leaks, bursts, or tampering activity.

The communication module 268 may transmit the acoustic data to thecomputing host (e.g., computing host 120 of FIG. 1 and/or computing host320 of FIG. 3). The communication module 268, which may comprise a datareceiver, a data transmitter, a data transceiver, and/or an antenna maybe used to wirelessly transmit and/or receive signals, commands, and/ordata to and from other devices, including the computing host via anetwork and/or a communications hub across a communication link orlinks.

In examples, the computing node 250 may comprise other components which,although not illustrated, may comprise a power supply, a data receiver,an antenna, an input device, additional sensors, etc.

FIG. 3 illustrates a computing system including a computer-readablestorage medium 330 storing instructions 332-336 to analyze datacollected within a fluid distribution system according to examples ofthe present disclosure. The computer-readable storage medium 330 isnon-transitory in the sense that it does not encompass a transitorysignal but instead is made up of one or more memory componentsconfigured to store the instructions 332-336. The computer-readablestorage medium 330 may be representative of a memory resource and maystore machine executable instructions 332-336, which are executable on acomputing system such as computing host 120 of FIG. 1 as well as thecomputing host 320 of FIG. 3 in conjunction with processing resource322.

In the example shown in FIG. 3, the instructions 332-336 comprise soundpropagation comparison with automated frequency selection determininginstructions 332, pipe-specific sound attenuation ranges determininginstructions 334, and node location determining utilizing graphicalmapping instructions 336. The instructions 332-336 of thecomputer-readable storage medium 330 may be executable so as to performthe techniques described herein, including the functionality describedregarding the method 500 of FIG. 5, the method 900 of FIG. 9, and themethod 1500 of FIG. 15.

For example, the sound propagation comparison with automated frequencyselection determining instructions 332 may correspond to blocks 502-512of FIG. 5. The pipe-specific sound attenuation ranges determininginstructions 334 may correspond to blocks 902-908 of FIG. 9. Finally,the node location determining utilizing graphical mapping instructions336 may correspond to blocks 1502-1508 of FIG. 15. The functionality ofthese instructions 332-336 is described below with reference to thefunctional blocks of FIGS. 5,9, and 15, respectively, but should not beconstrued as so limiting.

FIG. 4 illustrates diagram 400 of a fluid distribution system 410 withcomputing nodes 450A-450C (also referred to herein generally ascomputing nodes 450) for collecting and analyzing acoustic data withinthe fluid distribution system according to examples of the presentdisclosure. The fluid distribution system 410 may comprise pipes andother components (e.g., valves, couplings, fittings, meters, hydrants,etc.) used to carry fluids (e.g., water, gas, etc.) such as to customerlocations. According to various aspects of the present disclosure,computing nodes 450 may be attached to the fire hydrants 402A-402C (alsoreferred to herein generally as fire hydrants 402). In some aspects,computing nodes 450 may be attached to each hydrant 402 while otheraspects may include attachment with about every other one of thehydrants 402. In FIG. 4, for example, three adjacent fire hydrants402A-C are shown, connected to a pipe of the fluid distribution system410 for detecting a noise, such as noise source 420A or leak 420B.Because of the nature of a water leak, such as leak 420B, acousticsignals or vibration signals can be detected on the components (e.g.,pipes, fire hydrants 402, etc.) of the fluid distribution system 410.Particularly, computing nodes 450 may be mounted on the pipes themselvesor may be mounted on the hydrants 402. Optionally, when two leakdetectors, adjacent on the fluid distribution system 410 such ascomputing nodes 450 mounted on hydrants 402 nearest to the leak 420B,are able to pick up acoustic signals with sufficient strength, thesignals may be used to detect the presence and location of a leak.Alternatively, a computing node 450 may be located in a meter, inanother communication device, as a stand-alone unit, or in any otherpiece of utility equipment that interfaces with the fluid distributionsystem 410.

According to some aspects, an intentional noise may be implemented, suchas noise source 420A, in order to gather acoustic data between twocomputing nodes 450. For example, a user may be acquiring data in orderto analyze a segment of a pipe for condition assessment. The pipesegment, for example, may be between points A and B on FIG. 4. The usermay be collecting and analyzing data from computing node 450A on hydrant402A and computing node 450B on hydrant 402B, and generating noisesource 420A by hitting hydrant 402C with a hammer, for example. Thesound velocity 422A-B from noise source 420A is then propagated throughthe pipe and the computing nodes 450 than collect the acoustic datacreated by the noise source 420A. This data is then analyzed and a speedof sound in the water-filled pipe is computed, as further discussedherein.

FIG. 5 illustrates a flow diagram of a method 500 to analyze datacollected within a fluid distribution system and determine pipedegradation utilizing predicted frequency content according to examplesof the present disclosure. The method 500 may be executed by a computingsystem or a computing device such as computing host 120 of FIG. 1. Themethod 500 may also be stored as instructions on a non-transitorycomputer-readable storage medium such as computer-readable storagemedium 330 of FIG. 3 that, when executed by a processing resource (e.g.,processing resource 122 of FIG. 1 and/or processing resource 322 of FIG.3), cause the processing resource to perform the method 500.

At block 502, the method 500 begins and comprises receiving criteria fora pipe segment in order to determine condition assessment by calculatingpipe degradation for the pipe segment. An exemplary pipe segment may beillustrated in FIG. 4 as the pipe segment between points A and B.According to some aspects, the criteria may be manually entered by atechnician or another user of the system. According to other aspects,these values may be automatically populated by the computing host asknown information for a pipe segment as stored in a table. According toother aspects, these values may be automatically calculated. The pipesegment criteria may include pipe characteristics, watercharacteristics, and a length of the pipe segment (e.g., a distance Dbetween computing nodes 450A and 450B, as shown in FIG. 4). The pipecharacteristics may comprise a young modulus, an inner diameter, a wallthickness, and a lining thickness. The water characteristics maycomprise a bulk modulus, a temperature, a background pressure, and abackground velocity.

Next, at block 504, the method 500 comprises determining a theoreticalspeed of sound based on the pipe segment criteria that was received atblock 502 utilizing equations known in the art for calculatingpropagation velocity of acoustic waves in a pipe. For example,propagation velocity of acoustic waves in an unbounded fluid body may bedefined by the following equation:

$v_{0} = \sqrt{\frac{K}{\rho}}$where K is the bulk modulus of elasticity of the fluid and ρ is itsdensity. According to some aspects, a velocity of acoustic waves forthin-walled pipe with a uniform cross-section may be calculated, and isdefined by the following equation:

$v = \frac{v_{0}}{\sqrt{1 + {c\frac{DK}{tE}}}}$where D is the diameter of the pipe, t is the wall thickness, E is theelastic modulus of the pipe material, and c is a factor that takes intoaccount the fixation method of the pipe. According to some aspects, thevelocity of acoustic waves for a thick-walled pipe with expansion jointsthroughout its length may be calculated, and is defined by the followingequation:

$v = \frac{v_{0}}{\sqrt{1 + {\frac{DK}{tE}\left( {{\frac{2t}{D}\left( {1 + \mu} \right)} + \frac{D}{D + t}} \right)}}}$where μ is the Poisson's ratio of the pipe material. According to someaspects, the velocity of acoustic waves for a thick-walled pipe withconstrained axial movement may be calculated, and is defined by thefollowing equation:

$v = \frac{v_{0}}{\sqrt{1 + {\frac{DK}{tE}\left( {{\frac{2t}{D}\left( {1 + \mu} \right)} + {\frac{D}{D + t}\left( {1 - \mu^{2}} \right)}} \right)}}}$

Next, at block 506, the method 500 comprises determining a prediction offrequency content based on the pipe segment criteria from block 502, andthe calculated theoretical speed of sound from block 504. Examples ofprediction of frequency content are illustrated in FIGS. 6A-6D.

FIGS. 6A-6D illustrate graphs 600A-600D of data relating to thetechniques for collecting and analyzing data and creating predictionmodels within a fluid distribution system according to examples of thepresent disclosure. In particular, FIGS. 6A and 6B illustrate graphs600A and 600B, respectively, of measured frequency content data within afluid distribution system according to examples of the presentdisclosure. For example, graph 600A illustrates measured frequencycontent of a cast iron (“CI”) pipe, with a diameter of 8 inches and alength of 330 feet. Graph 600B illustrates measured frequency content ofa ductile iron (“DI”) pipe, with a diameter of 8 inches and a length of811 feet.

FIGS. 6C and 6D illustrate graphs 600C and 600D, respectively, ofpredicted frequency content data relating to the techniques forcollecting and analyzing data within a fluid distribution systemaccording to examples of the present disclosure. In particular, FIGS. 6Cand 6D illustrate a cross-spectrum density (CSD) function characterizingthe acceleration of the wall that has the shape of a band-pass filter.According to some aspects, the band-pass filter may be a combination ofa fixed high-pass cut-off and a variable low-pass cut-off for differentdistances.

According to some aspects, the pipe system may act as a low-pass filteras higher frequencies attenuate quicker, where the acoustic pressurewave may propagate along the pipe system and may be attenuated as ittravels away from the source. The attenuation, as described furtherherein, may depend on factors such as the distance, the frequency, thelosses in the wall (or damping) and the soil attenuation. Thus, thecut-off frequency of this low-pass filter, may depend on distance fromthe source, properties of the wall material, and soil composition.According to some aspects, the pressure wave in the water mediumtransfers to the wall, where the wall acts as a spring-mass system whichbehaves as a high-pass filter. Thus, the cut-off frequency depends onthe elastic properties of the wall material.

According to an exemplary aspect, graph 600C illustrates predictedfrequency content of the same pipe illustrated in FIG. 6A, a CI pipe,with a diameter of 8 inches and a length of 330 feet. Graph 600Dillustrates predicted frequency content of the same pipe illustrated inFIG. 6B, a DI pipe, with a diameter of 8 inches and a length of 811feet.

Referring back to FIG. 5, after a prediction for frequency content isdetermined at block 506, next, at block 508, the method 500 comprisesdetermining a suggested frequency range for an acoustic propagationdetection system, or the like, to utilize to measure actual speed ofsound based on the prediction of frequency content. An example ofdetermining a suggested frequency range is illustrated in FIG. 7.

FIG. 7 illustrates graph 700 of data relating to the techniques forcollecting and analyzing data and creating prediction models within afluid distribution system according to examples of the presentdisclosure. In particular, FIG. 7 illustrates graph 700 of hypotheticalfrequency content data in order to illustrate how a suggested frequencyrange may be calculated. For example, first an explicit frequency ispredicted for a specific type of pipe using the following analyticalequation:

$f_{pred} = \sqrt{\frac{v^{2}}{\pi\;{dD}}}$where v is the speed of sound in the pipe (velocity of acoustic waves),d is the distance between the acoustical sensors, and D is the diameterof the pipe. Following the calculation for an explicit frequency, asuggested frequency range may be calculated. and is defined by thefollowing equation:[α*f _(pred) ;β*f _(pred)]where α and β are two empirical parameters that may be identified usinga statistical analysis on the attenuation of pipes.

Referring back to FIG. 5, after a suggested frequency range isdetermined at block 508, next, at block 510, the method 500 comprisesmeasuring an actual speed of sound with an acoustic propagationdetection system for the pipe segment utilizing the suggested frequencyrange calculated at block 508. According to some aspects, the acousticpropagation detection system may be an ECHOLOGICS® ECHOSHORE®-DX leakdetection system, or other known systems in the art for measuring speedof sound in a pipe segment between two nodes of a utility system.According to some aspects, a speed of sound in a water-filled pipe frommeasurement (v_(m)) may be calculated, and is defined by the followingequation:

$v_{m} = \frac{d}{\Delta\; t}$where d is the distance between two acoustical sensors, such ascomputing nodes 450A and 450B, and Δt is the time delay between thesignals detected by the same two acoustical sensors, such as soundvelocity 422A acquired by computing node 450A, and sound velocity 422Bacquired by computing node 450B, as illustrated in FIG. 4. According tosome aspects, Δt may be obtained using a correlation function known inthe art.

Finally, at block 512, the method 500 comprises determining pipedegradation based on loss of the pipe wall thickness of the pipe segmentbased on the actual speed of sound as measured at block 510, and thetheoretical speed of sound calculated at block 504. According to someaspects, the pipe degradation based on loss from the pipe wall thicknessmay be calculated by using the appropriate equation for the velocity ofacoustic waves in pipes as described herein. In that case, the remainingwall thickness t_(rem) is a function depending on the parameters D, K,E, c, μ, v_(m) and v₀. The pipe degradation, in percent, (DEG_(%)) iscalculated by comparing the current wall thickness (remaining thickness)to the original wall thickness (nominal thickness, t_(nom)) with thefollowing equation:

${DEG}_{\%} = {100*\left( \frac{t_{rem} - t_{nom}}{t_{nom}} \right)}$

FIG. 8 illustrates a screen diagram of a user interface to analyze datacollected within a fluid distribution system according to examples ofthe present disclosure. In particular, FIG. 8 illustrates an examplescreenshot 800 relating to the method 500 described above in regard toFIG. 5 for implementing a method to determine pipe degradation utilizingpredicted frequency content. In this example aspect, as discussed hereinfor block 502 of method 500, a user can manually enter in the pipesegment criteria as pipe characteristics, water characteristics, and alength of the pipe segment (“Distance between sensors”) in the fields ofblock 802. Once the pipe segment criteria has been entered, a user mayselect the first compute option 804, and the system will calculate anddisplay a theoretical speed of sound (block 806), determine a predictionof frequency content, and calculate and display a suggested frequencyrange (block 808) as discussed herein for blocks 504-508 of method 500.According to the example aspect, a user may then enter the suggestedfrequency range from block 808 into their acoustic propagation detectionsystem, and enter the measured speed of sound into field 810 asdiscussed herein for block 510 of method 500. Once the pipe segmentcriteria has been entered, and an actual speed of sound has beenentered, a user may select the second compute option 812, and the systemwill calculate and display a predicted pipe degradation (block 814) asdiscussed herein for block 512 of method 500.

According to some aspects, the acoustic propagation detection systemused to measure the speed of sound may communicate with the pipedegradation calculation system, and may automatically measure the speedof sound after the suggest frequency range has been calculated anddisplay the measurement on field 810. According to some aspects, theacoustic propagation detection system and the pipe degradationcalculation system may be the same system, and thus may alsoautomatically measure the speed of sound after the suggested frequencyrange has been calculated and display the measurement on field 810.

FIG. 9 illustrates a flow diagram of a method 900 to collect and analyzedata to generate and utilize pipe-specific sound attenuation rangeswithin a fluid distribution system according to examples of the presentdisclosure. The method 900 may be executed by a computing system or acomputing device such as computing host 120 of FIG. 1. The method 900may also be stored as instructions on a non-transitory computer-readablestorage medium such as computer-readable storage medium 330 of FIG. 3that, when executed by a processing resource (e.g., processing resource122 of FIG. 1 and/or processing resource 322 of FIG. 3), cause theprocessing resource to perform the method 900.

At block 902, the method 900 begins and comprises receivingpredetermined criteria for a specific pipe-soil combination. Accordingto some aspects, the predetermined criteria may be manually entered by atechnician or another user of the system. According to other aspects,these values may be automatically populated by the computing host asknown information for a pipe segment as stored in a table. Thepredetermined criteria may comprise damping within the pipe wall (η),the diameter of the pipe (D), the Bulk modulus of water (K), the elasticmodulus of the pipe material (E), the wall thickness (t), and thefree-field water wavespeed (c₀). According to some aspects, thefree-field water wavespeed c₀ may comprise the propagation velocity ofacoustic waves in an unbounded fluid body.

Next, at block 904, the method 900 comprises determining an attenuationbased on the pipe-soil criteria that was received at block 902. Forexample, predicting pipe attenuation based on the type of pipe and soiltype may be determined by calculating a specific combination pipe-soilattenuation coefficient (λ_(tot)). For an above ground pipe, theattenuation coefficient λ is related to the loss in the pipe-wall andmay be defined by the following equation:

$\lambda = {\frac{1}{v_{0}}\frac{\eta\;{{DK}/2}\;{Et}}{\sqrt{\left( {1 + \frac{DK}{Et}} \right)}}}$

According to aspects described herein, the surrounding medium, e.g. thesoil, may be considered as a virtual layer on the outside of the pipewall. Thus, the attenuation from the soil may be applied directly withthe loss in the pipe-wall to predict the overall attenuation of aspecific combination pipe-soil. The specific combination pipe-soilattenuation coefficient (λ_(tot)) may be defined by the followingequation:

$\lambda_{tot} = {\frac{1}{v_{0}}\frac{\left( {\eta + \eta_{soil}} \right){{DK}/2}{Et}}{\sqrt{\left( {1 + \frac{DK}{Et}} \right)}}}$where η_(soil) is the damping from the surrounding medium, e.g., thesoil. The different types of soil may be classified by soil code andsoil series. An example soil classification table for six types ofcommonly found soil, is illustrated in Table 1 below.

TABLE 1 Classification of soils Soil Code Soil series Soilclassification ADA Adrian sandy or sandy-skeletal mixed, esic, mesicTerric Haplosaprists CAB Catlin fine-silty, mixed, superactive mesic,Oxyaquic Argiudolls DRA Drummer fine-silty, mixed, superactive, mesic,Typic Endoaquolls MEA Medway fine-loamy, mixed, superactive, mesic,Fluvaquentic Hapludolls PLA Plainfield mixed, mesic Typic UdipsammentSAC Sable fine-silty, mixed, superactive mesic, Typic EndoaquollsEach type of soil may have a unique combination of four components:clay, silt, sand, and organic matter. Based on a review known in the artof several soil types listed in Table 1, the range of variation for theattenuation coefficient of soils was determined to be predominantly inthe following range: 0.3<η_(soil)<1. According to some aspects, if thespecific soil classification where its pipe network is buried is known,a specific attenuation coefficient for that specific soil type may bedetermined.

According to some aspects, for a specific combination pipe-soil, theattenuation (A) is a function of the specific combination pipe-soilattenuation coefficient (λ_(tot)), a frequency, and a distance from thesource, and may be defined by the following equation:A=e ^(−λ) ^(tot) ^(ωd)

where ω, the angular frequency, and d, the distance from the source, aretwo variables. According to some aspects, utilizing this equation acolored graphical illustration (or “colormap” as described herein) maybe obtained where ω varies along the x-axis and d varies along they-axis. For any combination of these two variables, the equation mayprovide a value of attenuation which may be colored to provide thecolormap. The attenuation in dB (A_(dB)) may be defined by the followingequation:A _(dB)=20 log₁₀(A)

Next, at block 906, the method 900 comprises employing the attenuationdata from block 904 to produce a colormap of sound attenuation. Examplesof colormaps and an illustration for propagating distances for differenttypes of pipes are illustrated in FIGS. 10-14.

FIGS. 10-14 illustrate graphs 1000, 1100A,B, 1200A,B, 1300A,B, and 1400of data relating to the techniques for collecting and analyzing data togenerate and utilize pipe-specific sound attenuation ranges within afluid distribution system according to examples of the presentdisclosure. In particular, graphs 1000, 1100A,B, 1200A,B, 1300A,B, and1400 may represent data analyzed using the method 900, for example.

FIG. 10 illustrates graph 1000 as an example colormap with utilizationof the formulation in dB to visualize attenuation data. For thecolormaps described herein, the “Green” area 1002 represents anattenuation below 40 dB, the “Yellow” area 1004 represents anattenuation between 40 and 60 dB, and the “Red” area 1006 represents anattenuation above 60 dB. According to aspects described herein, by usingthe colormap visualization, it is possible to identify equivalentpropagating distances for specific types of pipes and different soiltypes. It will be appreciated by one skilled in the art that thecolormaps described herein, e.g. FIGS. 10-14 are shown as black andwhite in the drawings, but represent actual colors on a computer screenor display. For example, “Green” area 1002 would be green, “Yellow” area1004 would be yellow, and “Red” area 1006 would be red on an exemplarycomputer display.

FIGS. 11-13 illustrate colormap graphs for a specific type and diameterof pipe using high attenuation from soil (e.g., 0.3) to identify theshortest propagating distance, and using low attenuation from soil(e.g., 0.1) to identify the longest propagating distance. For example,FIG. 11A illustrates graph 1100A as a colormap for a cast iron pipe, sixinches in diameter, with high attenuation from the soil to identify theshortest distance of propagation. Therefore, at 90 Hz (the specificfrequency for metallic pipes used by the acoustic propagation detectionsystem, ECHOSHORE®-DX, as described herein), the shortest distance forpropagation (located at approximately 50 dB in the “Yellow” area 1004)is approximately 580 feet. Similarly, FIG. 11B illustrates colormap1100B for the same cast iron pipe, six inches in diameter, but now thelongest distance of propagation is identified using low attenuation fromthe soil. Therefore, at 90 Hz, the longest distance for propagation(located at approximately 40 dB in the “Yellow” area 1004) for a castiron pipe, 6 inches in diameter, is approximately 1000 feet.

FIG. 12A illustrates graph 1200A as a colormap for a steel pipe, teninches in diameter, with high attenuation from the soil to identify theshortest distance of propagation. Therefore, at 90 Hz (the specificfrequency for metallic pipes used by the acoustic propagation detectionsystem, ECHOSHORE®-DX, as described herein), the shortest distance forpropagation (located at approximately 50 dB in the “Yellow” area 1004)is approximately 750 feet. Similarly, FIG. 12B illustrates colormap1200B for the same steel pipe, ten inches in diameter, but now thelongest distance of propagation is identified using low attenuation fromthe soil. Therefore, at 90 Hz, the longest distance for propagation(located at approximately 40 dB in the “Yellow” area 1004) for a castiron pipe, 6 inches in diameter, is approximately 1300 feet.

FIG. 13A illustrates graph 1300A as a colormap for a plastic pipe, eightinches in diameter, with high attenuation from the soil to identify theshortest distance of propagation. Therefore, at 10 Hz (the specificfrequency for plastic pipes used by the acoustic propagation detectionsystem, ECHOSHORE®-DX, as described herein) the shortest distance forpropagation (located at approximately 50 dB in the “Yellow” area 1004)is approximately 340 feet. Similarly, FIG. 13B illustrates a colormap1300B for the same plastic pipe, eight inches in diameter, but now thelongest distance of propagation is identified using low attenuation fromthe soil. Therefore, at 10 Hz the longest distance for propagation(located at approximately 40 dB in the “Yellow” area 1004) for a plasticpipe, 8 inches in diameter, is approximately 585 feet.

Referring back to FIG. 9, after the colormap for the specific pipe isproduced at block 906, next, at block 908, the method 900 comprisesdetermining a propagating distance for the specific pipe-soilcombination based on the colormap and a predetermined frequency, asillustrated by FIGS. 11-13.

FIG. 14 illustrates a graph 1400 of example propagating distances fordifferent types of pipes based on attenuation as calculated using themethod 900, for example. For each type of pipe, the parameters of aspecific pipe are entered into a computing system or a computing devicesuch as computing host 120 of FIG. 1. Then, the longest distance a soundcan propagate may be calculated using the soil with the leastattenuation (η_(soil)=0.3), and the shortest distance of propagation maybe calculated using the soil with the highest attenuation (η_(soil)=1),and is defined by the following equation:

$d_{propagation} = \frac{A_{dB}*{\ln(10)}}{20*\lambda_{tot}*\omega_{p}}$where ω_(p) is a predetermined frequency, either selected by the userduring a manual process or fixed by the settings of a leak detectionsystem.

According to some aspects, the shortest distance may be calculated bythe intersection between a specific frequency and the middle of the“Yellow” area 1404 which corresponds to an attenuation of ˜50 dB. Thelongest distance may be calculated by using the intersection between aspecific frequency and the bottom of the “Yellow” area 1404 whichcorresponds to an attenuation of ˜40 dB. According to some aspects 60 dBmay be used to calculate the shortest distance, however 50 dB is used inthe exemplary aspect because it is based on the physical limitations ofthe leak detection system and its electronics related to the ability todistinguish a sound source from a background noise. Thus, if the soundsource is attenuated with more than 50 dB, a typical system may not beable to discriminate between the two. Current state of the art producesacoustic sensors and electronics with a dynamic range of 40 dB to 60 dB,therefore, 50 dB is used as an exemplary aspect. For example, if theattenuation is below 40 dB, it may be expected that the sound source isalways detectable because the sound level from the source is stillstrong enough. However, if the attenuation is above 60 dB, it may beexpected that the sound source is not detectable anymore because thesound level is below detectable level, therefore, the 40 dB to 60 dBarea is the uncertainty zone, e.g. “Yellow” area 1404.

According to an exemplary aspect as illustrated in FIG. 14, the “Green”area(s) 1402 for each example pipe tested (e.g. steel, ductile iron,cast iron, asbestos cement, and plastic) corresponds to an attenuationof below 40 dB, the “Yellow” area(s) 1404 represents an attenuationbetween 40 and 60 dB, and the “Red” area(s) 1406 represents anattenuation above 60 dB. It will be appreciated by one skilled in theart that the graph 1400, described herein for FIG. 14, is shown as blackand white in the drawing, but represents actual colors on a computerscreen or display. For example, “Green” area(s) 1402 would be green,“Yellow” area(s) 1404 would be yellow, and “Red” area(s) 1406 would bered on an exemplary computer display.

According to the exemplary aspect, for metallic pipes, the specificfrequency used may be 90 Hz, and for plastic pipes, the specificfrequency used may be 10 Hz. Each specific frequency in this exemplaryaspect is corresponding to the settings of the ECHOSHORE®-DX system.However, the distances that are obtained using this approach aredependent on the frequency used, and these frequencies depend on thecharacteristics of the acoustic technology used to listen to the pipes.Therefore, according to other aspects, another acoustic propagationdetection system known in the art may be used that utilize differentfrequencies for metallic and/or plastic pipes, thus the frequency to beused should be adapted based on the specific characteristics of theacoustic propagation detection system used.

According to the exemplary aspect, the associated values used to createFIG. 14 are dependent on the settings of the acoustical monitoringsystem and its sensitivity to sound. Therefore, it will be appreciatedby one skilled in the art that it is expected that the values ofdistance for acoustical coverage will increase as a result ofimprovements made on the acoustical monitoring system, improving itssensitivity to sound.

FIG. 15 illustrates a flow diagram of a method 1500 to analyze datacollected within a fluid distribution system and determine computingnode location selection utilizing graphical mapping according toexamples of the present disclosure. The method 1500 may be executed by acomputing system or a computing device such as computing host 120 ofFIG. 1. The method 1500 may also be stored as instructions on anon-transitory computer-readable storage medium such ascomputer-readable storage medium 330 of FIG. 3 that, when executed by aprocessing resource (e.g., processing resource 122 of FIG. 1 and/orprocessing resource 322 of FIG. 3), cause the processing resource toperform the method 1500.

At block 1502, the method 1500 begins and comprises receivingpredetermined criteria for each pipe segment of a plurality of pipesegments in a fluid distribution system for a given geographical area.According to some aspects, the predetermined criteria may be manuallyentered by a technician or another user of the system. According toother aspects, these values may be automatically populated by thecomputing host as known information for a pipe segment as stored in atable. The predetermined criteria may comprise a pipe material, a pipediameter, and a length of a pipe segment.

Next, at block 1504, the method 1500 comprises determining an equivalentlength for each pipe segment based on the pipe segment criteria that wasreceived at block 1502. For example, to obtain a graphical map,geographical distance between two possible locations for installation ofcomputing nodes is needed. According to aspects described herein, thedistance between two possible locations for installation of computingnodes is defined as segment length. Depending on the type of pipe andpipe diameter, a coefficient (y) is applied to the segment length tocalculate an equivalent length, and may be defined by the followingequation:Equivalent_Length=γ*Segment_Lengthwhere γ is a coefficient dependent on the type of pipe and pipematerial. An example table of a list of coefficients for different typesof pipes and materials is illustrated in Table 2 below.

TABLE 2 Coefficient for different types of pipes and materials SteelDuctile Iron Cast Iron Asbestos Cement Plastic 6 in 1.000 1.100 1.3001.600 2.100 8 in 1.059 1.165 1.377 1.695 2.224 10 in 1.108 1.218 1.4401.772 2.326 12 in 1.149 1.264 1.493 1.838 2.412According to aspects described herein, a 6-inch diameter steel pipe maybe used as a reference pipe, thus the coefficient y is 1.

Next, at block 1506, the method 1500 comprises grouping each pipesegment into a specific category of a plurality of categories based onthe equivalent lengths. According to some aspects, the plurality ofcategories may comprise the following: Green, Yellow, and Red. Theshortest distance and longest distances of propagation for equivalentlength may be calculated, and then the system may group each pipesegment into one of three separate equivalent length categories: Green,Yellow, and Red. For example, the Green category may represent acalculated equivalent length of less than or equal to 750 feet, theYellow category may represent a calculated equivalent length of greaterthan 750 feet and less than or equal to 1300 feet, and the Red categorymay represent a calculated equivalent length of greater than 1300 feet.According to aspects described herein, the Green category may representpipe segments (lengths of pipe between two computing nodes) that thesystem determines that each computing node would be very likely todetermine if a leak is present based on the propagation between the twocomputing nodes. According to aspects described herein, the Yellowcategory may represent pipe segments the system determines that eachcomputing node would be somewhat likely be able to determine if a leakis present based on the propagation distance between the two computingnodes. According to aspects described herein, the Red category mayrepresent pipe segments that the system determines that each computingnode would not be very likely to determine if a leak is present based onthe propagation distance (the equivalent length calculated) between twocomputing nodes.

According to aspects described herein, travelling distances for eachtype of pipe and pipe material, e.g. FIGS. 11-13, can be reverselydetermined by dividing 750 feet (shortest distance of propagation forthe reference pipe—6-inch diameter steel pipe with a coefficient of1.000) by the corresponding coefficient from Table 2 to obtain theshortest distance of propagation, and dividing 1300 feet (longestdistance of propagation for the reference pipe—6-inch diameter steelpipe with a coefficient of 1.000) by the corresponding coefficient fromTable 2 to obtain the longest distance of propagation. For example,FIGS. 11A-B illustrate colormaps of attenuation for a 6-inch diametercast iron pipe, and a 6-inch diameter cast iron pipe has a coefficient yof 1.3 (Table 2); therefore, the shortest distance of propagation wouldbe 750 feet divided by 1.3 (y), which equals ˜577 feet, and the longestdistance of propagation would be 1300 feet divided by 1.3 (y), whichequals 1,000 feet.

Finally, at block 1508, the method 1500 comprises creating a graphicalmap of the plurality of pipe segments and utility components utilizing aplurality of links. According to some aspects, the plurality of linksmay comprise distinct visual indications based on the propagationcategory for each pipe segment. In addition, each utility component maycomprise a corresponding computing node, such as such as computing nodes450A and 450B. Examples of graphical mapping for propagating distancesfor a network of pipes in a given geographical area utilizing aplurality of links are illustrated in FIGS. 16A-16C.

FIGS. 16A-16C illustrate diagrams 1600A-1600C of a fluid distributionsystem 1604 with computing nodes 1602 for collecting and analyzingacoustic data for graphical mapping of computing node location selectionwithin the fluid distribution system according to examples of thepresent disclosure. The fluid distribution system 1604 comprises pipesand other components (e.g., valves, couplings, fittings, meters,hydrants, etc.) used to carry fluids (e.g., water, gas, etc.) such as tocustomer locations. The fluid distribution system 1604 may comprisecomputing nodes 1602 located at each hydrant, such as computing nodes150 of FIG. 1, computing node 250 of FIG. 2, and/or computing nodes 450of FIG. 4. In particular, the graphical map in diagram 1600A of FIG. 16Arepresents a given geographical area of a fluid distribution system 1604with computing nodes 1602 installed at each hydrant.

According to some aspects, the physical pipe network informationillustrated in FIG. 16A may be generated using geographic informationsystem (GIS) data sourced from the customer's documentation whereas thegraphical mapping of computing node location selection may be determinedthrough a two-step process, and is shown in FIG. 16B. First, the systemmay automatically determine equivalent length propagation based on pipesegment criteria and divide those into three different propagationcategories (Green, Yellow, and Red), as described by method 1500.Second, the system may generate a plurality of links that correlate tothe three categories and may be overlaid on a pipe network map. Forexample, as illustrated in FIG. 16B (with a background street map) andFIG. 16C (without a background street map), three different types ofplurality of links are shown: a solid line 1610 which may represent theGreen category; a dashed line 1612 which may represent the Yellowcategory; and a dotted line 1614 which may represent the Red category.It will be appreciated by one skilled in the art that the plurality oflinks shown in diagrams 1600B,C, described herein for FIG. 16B,C, areshown as black and white lines (e.g. solid, dashed, and dotted) in thedrawing, but represent actual colors on a computer screen or display.For example, a solid line 1610 may be shown as a solid green line, adashed line 1612 may be shown as a solid yellow line, and a dotted line1614 may be shown as a solid red line on an exemplary computer display.

According to some aspects, efficient computing node placement may beautomatically determined and mapped out in order to determine whichcomputing nodes may not be necessary to be installed on each hydrant andstill be able to meet requirements for propagation and proper acousticcoverage (e.g., the Green and Yellow categories). According to someaspects, the system may determine where to place additional computingnodes for adequate acoustical propagation for leak detection if it isdetermined the equivalent length is too long to ensure propagation todetect a leak (e.g., the Red category). According to some aspects, thesystem may determine a specific computing node is not required for aspecific utility component in order to ensure proper propagationmeasurement for leak detection. According to further aspects, theplurality of links on the graphical map may be automatically updatedwithout the specific utility component for visual display to the user.Alternatively, the system may highlight the specific node that may beremoved, allowing the user to determine if the system should remove thespecific node, and if the user chooses to proceed, than the system wouldautomatically update the graphical map and plurality of linksaccordingly.

According to some aspects, a user may use the system to toggle betweenthe three different views of FIGS. 16A-C in order to acquire a bettervisualization the plurality of links with the map of the specificgeographic region being analyzed for computing node selection location.According to some aspects, a user may select a particular computing node1602 to be removed, and the system may automatically update theplurality of links such that the user can visualize the updated map withthe specific computing node 1602 removed in order to determine ifefficient propagation remains.

It should also be understood that conditional language, such as, amongothers, “can,” “could,” “might,” or “may,” unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain examples comprise, while otherexamples do not comprise, certain features, elements and/or steps. Thus,such conditional language is not generally intended to imply thatfeatures, elements, and/or steps are in any way required for one or moreparticular examples or that one or more particular examples necessarilycomprise logic for deciding, with or without user input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular example.

It should be emphasized that the above-described examples are merelypossible examples of implementations and set forth for a clearunderstanding of the present disclosure. Many variations andmodifications may be made to the above-described examples withoutdeparting substantially from the spirit and principles of the presentdisclosure. Further, the scope of the present disclosure is intended tocover any and all appropriate combinations and sub-combinations of allelements, features, and aspects discussed above. All such appropriatemodifications and variations are intended to be included within thescope of the present disclosure, and all possible claims to individualaspects or combinations of elements or steps are intended to besupported by the present disclosure.

What is claimed is:
 1. A method for creating a graphical mapping of pipenode location selection for a fluid distribution system, comprising:receiving predetermined criteria for each pipe segment of a plurality ofpipe segments in the fluid distribution system, each pipe segmentcomprising a section of pipe between two utility components of the fluiddistribution system, each utility component comprising a correspondingcomputing node configured for leak detection via acoustical propagation;determining an equivalent length for each pipe segment based at least onthe predetermined criteria; grouping each pipe segment into a specificpropagation category of a plurality of propagation categories based onthe equivalent lengths; and creating a graphical map of the plurality ofpipe segments and utility components utilizing a plurality of links, theplurality of links comprising distinct visual indications based on thepropagation category for each pipe segment, wherein the plurality ofpropagation categories comprises at least a first category if theequivalent length is acoustically covered to ensure propagation, asecond category if the equivalent length may be acoustically covered toensure propagation, and a third category if the equivalent length is toolong to ensure propagation.
 2. The method of claim 1, further comprisingdetermining where to place additional computing nodes for adequateacoustical propagation for leak detection if it is determined theequivalent length is too long to ensure propagation to detect a leak. 3.The method of claim 1, further comprising determining whether acomputing node is required for a specific utility component based on thegraphical map.
 4. The method of claim 3, wherein determining whether acomputing node is required for a specific utility component based on thegraphical map comprises determining a computing node is not required fora specific utility component and, subsequently, removing the specificutility component from the graphical map.
 5. The method of claim 1,further comprising automatically determining whether a computing node isrequired for each utility component to ensure acoustical coverage. 6.The method of claim 1, wherein the predetermined criteria for each pipesegment comprises at least a pipe material, a pipe diameter, and alength.
 7. The method of claim 1, wherein each utility component is ahydrant.
 8. A system for creating a graphical mapping of pipe nodelocation selection for a fluid distribution system, comprising: aplurality of computing nodes in fluid communication with the fluiddistribution system and configured to acquire acoustic data in the fluiddistribution system; and a computing host in communication with theplurality of computing nodes, the computing host programmed to performsteps comprising receiving predetermined criteria for each pipe segmentof a plurality of pipe segments in the fluid distribution system, eachpipe segment comprising a section of pipe between two utility componentsof the fluid distribution system, each utility component comprising acorresponding computing node configured for leak detection viaacoustical propagation; determining an equivalent length for each pipesegment based at least on the predetermined criteria; grouping each pipesegment into a specific propagation category of a plurality ofpropagation categories based on the equivalent lengths; and creating agraphical map of the plurality of pipe segments and utility componentsutilizing a plurality of links, the plurality of links comprisingdistinct visual indications based on the propagation category for eachpipe segment, wherein the plurality of propagation categories comprisesat least a first category if the equivalent length is acousticallycovered to ensure propagation, a second category if the equivalentlength may be acoustically covered to ensure propagation, and a thirdcategory if the equivalent length is too long to ensure propagation. 9.The system of claim 8, wherein the computing host is programmed tofurther perform the step of determining where to place additionalcomputing nodes for adequate acoustical propagation for leak detectionif it is determined the equivalent length is too long to ensurepropagation to detect a leak.
 10. The system of claim 8, wherein thecomputing host is programmed to further perform the step of determiningwhether a computing node is required for a specific utility componentbased on the graphical map.
 11. The system of claim 10, wherein thecomputing host is programmed to further perform the steps of determiningthat a computing node is not required for a specific utility componentand, subsequently, updating the plurality of links on the graphical mapwithout the specific utility component.
 12. The system of claim 8,wherein the computing host is programmed to further perform the step ofautomatically determining whether a computing node is required for eachutility component to ensure acoustical coverage.
 13. The system of claim8, wherein the predetermined criteria for each pipe segment comprises atleast a pipe material, a pipe diameter, and a length.
 14. The system ofclaim 8, wherein each utility component is a hydrant.
 15. Anon-transitory computer-readable storage medium storing instructionsthat, when executed by a processing resource, cause the processingresource to perform steps comprising: receiving predetermined criteriafor each pipe segment of a plurality of pipe segments in a fluiddistribution system, each pipe segment comprising a section of pipebetween two utility components of the fluid distribution system, eachutility component comprising a corresponding computing node configuredfor leak detection via acoustical propagation; determining an equivalentlength for each pipe segment based at least on the predeterminedcriteria; grouping each pipe segment into a specific propagationcategory of a plurality of propagation categories based on theequivalent lengths; and creating a graphical map of the plurality ofpipe segments and utility components utilizing a plurality of links, theplurality of links comprising distinct visual indications based on thepropagation category for each pipe segment, wherein the plurality ofpropagation categories comprises at least a first category if theequivalent length is acoustically covered to ensure propagation, asecond category if the equivalent length may be acoustically covered toensure propagation, and a third category if the equivalent length is toolong to ensure propagation.
 16. The non-transitory computer-readablestorage medium of claim 15, wherein the processing resource furtherperforms the step of determining where to place additional computingnodes for adequate acoustical propagation for leak detection if it isdetermined the equivalent length is too long to ensure propagation todetect a leak.
 17. The non-transitory computer-readable storage mediumof claim 15, wherein the processing resource further performs the stepof determining whether a computing node is required for a specificutility component based on the graphical map.
 18. The non-transitorycomputer-readable storage medium of claim 17, wherein determiningwhether a computing node is required for a specific utility componentbased on the graphical map comprises determining a computing node is notrequired for a specific utility component and, subsequently, removingthe specific utility component from the graphical map.